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    SeedQuant: A Deep Learning-based Census Tool for Seed Germination of Root Parasitic Plants

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    Thesis_Ramazanova.pdf
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    6.461Mb
    Format:
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    Embargo End Date:
    2021-05-02
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    Type
    Thesis
    Authors
    Ramazanova, Merey cc
    Advisors
    Ghanem, Bernard cc
    Committee members
    Wonka, Peter cc
    Thabet, Ali Kassem
    Program
    Computer Science
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2020-04-30
    Embargo End Date
    2021-05-02
    Permanent link to this record
    http://hdl.handle.net/10754/662732
    
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    Access Restrictions
    At the time of archiving, the student author of this thesis opted to temporarily restrict access to it. The full text of this thesis will become available to the public after the expiration of the embargo on 2021-05-02.
    Abstract
    Witchweeds and broomrapes are root parasitic weeds that represent one of the main threats to global food security. By drastically reducing host crops' yield, the parasites are often responsible for enormous economic losses estimated in billions of dollars annually. Parasitic plants rely on a chemical cue in the rhizosphere, indicating the presence of a host plant in proximity. Using this host dependency, research in parasitic plants focuses on understanding the necessary triggers for parasitic seeds germination, to either reduce their germination in presence of crops or provoke germination without hosts (i.e. suicidal germination). For this purpose, a number of synthetic analogs and inhibitors have been developed and their biological activities studied on parasitic plants around the world using various protocols. Current studies are using germination-based bioassays, where pre-conditioned parasitic seeds are placed in the presence of a chemical or plant root exudates, from which the germination ratio is assessed. Although these protocols are very sensitive at the chemical level, the germination rate recording is time consuming, represents a challenging task for researchers, and could easily be sped up leveraging automated seeds detection algorithms. In order to accelerate such protocols, we propose an automatic seed censing tool using computer vision latest development. We use a deep learning approach for object detection with the algorithm Faster R-CNN to count and discriminate germinated from non-germinated seeds. Our method has shown an accuracy of 95% in counting seeds on completely new images, and reduces the counting time by a signi cant margin, from 5 min to a fraction of second per image. We believe our proposed software \SeedQuant" will be of great help for lab bioassays to perform large scale chemicals screening for parasitic seeds applications.
    Citation
    Ramazanova, M. (2020). SeedQuant: A Deep Learning-based Census Tool for Seed Germination of Root Parasitic Plants. KAUST Research Repository. https://doi.org/10.25781/KAUST-VPG69
    DOI
    10.25781/KAUST-VPG69
    ae974a485f413a2113503eed53cd6c53
    10.25781/KAUST-VPG69
    Scopus Count
    Collections
    Theses; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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